Dips are geological bedding surfaces, such as sedimentary beds, fractures, faults, etc., which may or may not be flat or perpendicular to a wellbore. Dip information, e.g. azimuthal density images, obtained from well logs is an important source of information for structural analysis. It provides critical controls for reservoir modeling with important implication for STOOIP (Original Oil In Place). It is also basic input information to create earth models for modeling logging tool responses in petrophysical analysis.
The manual dip picking process is time-consuming and ergonomic-unfriendly. It usually takes many hours to process a well. The cost quickly builds up, and petrophysical evaluation can be heavily delayed, when a large number of wells needs to be processed. Besides, hand picking is subjective, yields higher uncertainty than automatic picking, and is affected by the image visualization mode, such as colormap, image value range, depth scale, etc.
Automatic dip picking takes advantage of the precise, tireless computational ability of a computer. Not only is it faster, lowers cost, avoids ergonomic issues, it is also more objective and reduces dip uncertainty. Petrophysicists need only to quality-check the results, focus on high-level interpretational work, and thus improve overall work quality.
Many automatic dip picking methods for dipmeter and pad-type image logs were developed since the 1950's. Many of these methods were published, including more than 20 patents since 1982, each of these methods tied to specific type of tools such as dipmeter tools or partial wellbore coveraged pad-type images. But they are unsuitable and unreliable to process full wellbore azimuthal image data.
More recently, LWD (logging while drilling) image data from conventional logging tools, such as gamma-ray, density, neutron, acoustic, and resistivity, etc., are extensively, routinely acquired in operations worldwide. No reliable automatic dip picking method is available for full wellbore image logs. The present invention addresses this technology gap. Although the present invention was originally designed for the full wellbore azimuthal type of images, it can also apply to any previous dipmeter and pad-type image logs, as will be explained below.
For old dipmeter logs consisting of 3, 4, 6 or 8 curves, the patents and publications listed next are based on two-curve (or pad-to-pad) correlation. The patents include: U.S. Pat. Nos. 4,316,250, 4,348,748, 4,355,357, 4,517,835, 4,541,275, 4,852,005, and 4,853,855. Publications include:                1. Sasseen, “An Electronic Analog Cross Correlator for Dip Logs,” Proc. IRE 37, No. 1, 10 (1957).        2. Moran et al., “Automatic Computation of Dipmeter Logs Digitally Recorded on Megnetic Tapes”, Journal of Petroleum Techology, July 1962, pp. 771-782 (1962).        3. Schoonover, “Computer Methods of Diplog Correlation,” Society of Petroleum Engineers Journal, Feb. 1973, pp. 31-38 (1973).        4. Hepp, “Cluster—A method for selecting the most probable dip results from dipmeter surveys,” Society of Petroleum Engineers, SPE 5543 (1975).        5. Vincent et al., “An Approach to Detailed Dip Determination Using Correlation by Pattern Recognition,” Journal of Petroleum Techology, 232-240 (1979).        6. Kemp, “An Algorithm for Automatic Dip Computation,” Computers & Geosciences 6, 193-209 (1980).        7. Kerzner, “An Analytical Approach to Detailed Dip Determination Using Frequency Analysis,” Society of Petrophysicists and Well Log Analysts, SPWLA 23rd Annual Logging Symposium, July 6-9, 1982, paper J.        8. Kerzner, “Formation Dip Determination—An Artificial Intelligence Approach,” The Log Analyst, September-October (1983), 10-22.        9. Easton, “Formation Dip Determination Using Digital Wellsite Processing Techniques,” Society of Petroleum Engineers, SPE 12181 (1983).        
These methods look for the depth shift generating maximum correlation value between each pair of dipmeter curves, and then find an optimal closure among all curves (i.e. fit a sine wave) to determine a best local sine wave. They differ from each other by their correlation methods and fitting optimization details. However, the present invention uses a different approach: the curves are correlated collectively using variances, not two by two, along sinusoidal lines and select the sine wave of minimum variance is selected without the need for sine wave fitting.
For image logs including pad-type and full-wellbore images, most of the patents and publications found in the literature (listed below) use edge detection and the Hough transform approach. These methods first find highly contrasted segments (or edges), then search for the best sine waves in the Hough (or parametric) space. Some other methods use other approaches such as frequency analysis, or edge detection combined with sine wave fitting, etc. They are detailed separately below. None of the methods uses a minimum-variance approach.
It will be explained below that the present invention first detects the global image sinusoidal trends, then examines detailed bed contrast to locate the bed boundaries. Most of the methods described in the literature used edge detection and Hough transform (or sine wave fitting). These methods are unreliable and highly affected by the noises on the image as they immediately focus on the detailed image features, which are highly affected by the noises, and overlook the global image patterns.
Methods using Hough transform include U.S. Pat. Nos. 5,162,994, 5,960,371, US 2005/0192753, U.S. Pat. No. 7,236,887[1], US 2011/0091078, and U.S. Pat. No. 7,136,510. Methods using frequency analysis include U.S. Pat. Nos. 5,983,163, 7,283,910, and 7,200,492. Methods using image local orientations include:                1. Van Genkel, “Robust Curve Detection using a Radon Transform in Orientation Space Applied to Fracture Detection in Borehole Images”, ASCI (2000).        2. Ye et al., 1997, “Automatic High Resolution Sedimentary Dip Detection on Borehole Imagery,” Society of Petrophysicists and Well Log Analysts, SPWLA paper “O” (second method in the paper: lamination plane detection).        
The Ye et al. 1997 paper uses a “linear” (straight-line) minimum-variance method to determine linear (straight-line) local pad-image orientation, then fit the tangent of the sine waves to the linear local pad-image orientations with a least-mean-square method. In contrast, the present invention uses minimum-variance along “sinusoidal lines” instead of sine wave fitting.
Methods using edge detection and sine wave fitting include U.S. Pat. Nos. 5,299,128, 6,266,618 and 6,226,595 and the following publications:    1. Antoine, “A Method to Derive Dips from Bedding Boundaries in Borehole Images,” SPE Formation Evaluation (1993).    2. Ye et al., “Automatic High Resolution Sedimentary Dip Detection on Borehole Imagery”, Society of Petrophysicists and Well Log Analysts, SPWLA paper O.—first method described in the paper: bed boundary detection—(1997).    3. Ye et al., “Automated Fracture Detection on High Resolution Resistivity Borehole Imagery”,Society of Petroleum Engineers, SPE 49300 (1998).
US Patent Publication No. 2012/0293178 describes a method using resistivity anisotropy and exhaustive search.